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Transcript of radiometric correction.ppt
7/22/2019 radiometric correction.ppt
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Radiometric Correction of
Image Data
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Radiometric Correction
Atmospheric correction attempts toquantify (i.e., remove) the effect of theatmosphere at the time an image was
acquired Geometric correction improves the
fidelity of pixel-DN location in an image
Radiometric correction improves thefidelity of the DN‟s that constitute an image
The purpose of radiometric correctionis to reduce the errors that may
confound scientific use of the DN’s
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Sources of Radiometric Noise
Radiometric errors constitute the “noise”in a remotely sensed signal (i.e., imagevariations not associated with actualvariations in the ground target of interest)
The errors include sensor related effects(e.g., electronic noise, dropped scan lines)
Spatial and/or temporal variations in thequantity or quality of illumination
(incoming irradiance) Surface properties (e.g., topographic
effects, sun glint)
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Reasons for Radiometric Correction
Correct for inconsistencies in image DN‟scaused by sensor errors or environmentalnoise
Normalize DN‟s between / among spectralchannels in the same image
Normalize DN‟s between / among multi-temporal images
Retrieve surface-energy properties suchas reflectance, albedo, groundtemperature, or other parameters
associated with scientific units of measure
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Errors Due to Sensor Problems Line Dropout
Striping or banding (detector out ofadjustment)
Line Start problems
Sensor Saturation
“Noisyimage”
Correctedimage
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Errors Due to Sensor Problems
Offset scan lines
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Landsat-7 Scan Line Corrector Failure
Error firstnoticedduringMay, 2003
Landsat-7 image of Railroad Valley, NV
http://spaceflightnow.com/news/n0307/27landsat7 /
Pre-SLCanomaly
Post-SLCanomaly
Post-SLCanomalyaftercorrectionalgorithm
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Scene-Illumination Adjustment
Scene Illumination• In satellite remote sensing, imagery
acquired at different times of the yearmay be required (e.g., to studyphenological cycle)
• These may require sun elevationcorrection and an earth-sun distance
correction
• Sun elevation correction accounts forthe seasonal position of the sunrelative to the earth
Image data acquired under differentsolar illumination angles need to benormalized to a constant solar position
Correction (next slide) ignorestopographic and atmospheric effects
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Correction for Differential Illumination
Solar Elevation: the angularelevation of the sun above thehorizon
Solar Zenith Angle: the angulardeviation from directly overhead (orthe complement of elevation)
Corrections:
DN ____
Sine Elevation Angle
orDN ____
Cosine Zenith Angle
This procedure normalizes the data to an overhead sun
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Scene-Illumination Adjustment
• Earth-Sun Distance Correction
Normalize for seasonal changes
in distance between earth & sun The irradiance from the sun
decreases as the square of theearth-sun distance
Earth-sun
distance during:
Aphelion: 94.8 million miles
(152.6 million kilometers)
Perihelion: 91.7 million
miles (147.5 millionkilometers)
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The DN’s Comprising Landsat Images
Following the launch of the Landsat-MSS (1972), many research andapplications projects were conducted
(and published) using raw DN‟s
Over time, investigators began tothink about the pixel-brightness
values (DN‟s). Questions followed….
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Questions About Image DN’s
What are they (and what aren‟tthey)?
Is it valid to compare DN‟s fromseveral different satellite systems(whether Landsat-5 versus Landsat-7or Landsat-7 versus Spot-5 or other
combinations)?
Is it valid to compare image datasetsfrom one time period to the next?
How do we make DN‟s truly useful?
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Answers to the Questions About DN’s
The DN‟s are unit-less numbers that serveas surrogates for target radiance. In fact,the DN‟s are linear with radiance, but
there is no scientific unit of measureassociated with them.
For scientific use, one must convert theDN‟s to some real physical unit (i.e.,
containing a meaningful unit of measure)
For example, the DN‟s can be converted toradiance (mW cm-2 sr-1) and/or
reflectance (%)
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The article thatcalled attentionto the problemof image DN‟s,with a focus onLandsat
1982
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LMax isradiancemeasured atdetectorsaturation
LMin is thelowest radiancemeasured bythe detector
QCalMax is themaximum DNpossible
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Publishedtables for usein converting
Landsat DN‟sto physicalvalues
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An Example of aProject Involving
the Normalizing ofImages to Cover aLarge Study Area
No conversion of DN‟sto radiance orreflectance wasnecessary
Rather, the need was
to normalize theradiometry in the 20Landsat-TM imagesto be used in amosaic and also forchange detection over
time
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Scene 1 Selection
Path/Row Optimal Period Dominant Land Cover (%) Selected Scenes (circa 1990) Selected Scenes (circa 2000)
19/36 May7-June17 Forest (76.95) 1988 May 30 2000 May 31
19/37 Apr23-June3 Forest (73.41) 1990 June 5 2001 May 18
19/38 Apr23-June17 Forest (56.18) 1991 June 8 2003 Apr 14
18/38 Apr23-June17 Forest (36.55) 1990 May 29 2000 Apr 30
19/39 Apr23-July1 Forest 42.38) 1990 Apr 18 2000 Apr 18
Scene 2 Selection
Path/Row Optimal Period Dominant Land Cover (%) Selected Scenes (circa 1990) Selected Scenes (circa 2000)
19/36 Sep24-Dec31 Forest (76.95) 1991 Sep 28 2000 Sep 28
19/37 Sep24-Dec32 Forest (73.41) 1991 Sep 28 2001 Oct 1
19/38 Oct8-Dec2 Forest (56.18) 1986 Oct 16 2001 Oct 14
18/38 Oct8-Dec31 Forest (36.55) 1991 Nov 24 2000 Oct 23
19/39 Nov5-Jan28 Forest 42.38) 1990 Nov 12 2000 Dec 17
Selected Landsat Scenes
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Radiometric Normalization of
Landsat Data
After visual interpretation, adjacentscenes appeared to have different ranges
of brightness values The variation is likely due to differing
atmospheric and illumination conditions
A radiometric normalization process was
used to correct the anomalies There was a need to adjust the brightness
values in each band to approximately thesame radiometric scale.
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Methods for Radiometric Normalization
Identify a „master scene‟ First order atmospheric correction (haze
reduction) Within areas of overlap between „master‟ and
„slave‟ scenes, identify pseudoinvariant features(PIF) Difference overlapping area between „master‟ and
„slave‟ scenes Calculate mean difference for each band based
on PIF‟s Apply the mean difference, (bias value) to the
entire slave sceneAdopted from Homer et al. (1997)
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Pseudoinvariant Feature Selection PIF‟s are targets that remain spectrally stable through time.
Examples include paved areas, rooftops, deep non-turbidwater, and dense evergreen forests.
Selection of 2 PIF‟s between common overlapping area of master and slavescene.
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Difference of Overlap Area
Band1 master - Band1 slave = Band1 difference
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Mean Difference of Overlap Area
The mean difference should ONLY be calculated using PIF‟s
This will eliminate inaccurate bias values as a result of spectraldissimilarities between features such as vegetation
Vegetative spectral characteristics change dramatically throughtime due to phenology.
The mean difference of the PIF‟s, (whichremain spectrally constant through time)
from master to slave image is about -62Brightness Values. This bias value can besubtracted from the entire slave sceneleaving approximately the sameradiometric scale as the master image.
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Before
radiometriccorrection
After
Notedifferencesin tone
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Statistical Results
To test if the pixels of the slave and master image aresimilar in brightness values, a 1 sample t-test wasperformed.
The difference between the overlapping area of a) the hazeadjusted master and b) the normalized slave scene wascomputed.
5,000 pixels were randomly sampled from the differencedimage.
If the radiometric normalization corrected dissimilarities inBV‟s between the images, then the mean difference of theoverlapping area should equal 0
Ho: μdiff = 0 Ha: μdiff ≠ 0 t-statistic -10.757 << 1.95 (95% significance) then Ho is
accepted, thus band 1 of the master and slave scenes arestatistically similar.
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Vendors Provide Some Corrections
Radiometrically corrected and calibratedspectral data in physical units at fullinstrument resolution as required
Radiometrically corrected data that have
also been spatially resampled Radiometrically corrected data with
temporal compositing
Radiometrically corrected data withconversion to specific physical orbiophysical parameters such as soilmoisture, leaf area index, absorbed
photosynthetically active radiation, etc.
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Summary
Radiometric corrections are needed to correct forerrors, such as detector anomalies, sometimesfound in image data
It is possible to correct for variable image-
illumination conditions; i.e., normalize imagery toa constant solar position (e.g., overhead)
Conversions of raw DN‟s to physical units isnecessary for scientific investigations
Normalizing of multiple images to a selectedmaster scene for a study area may be done usingpseudo-invariant ground features